Overview

Dataset statistics

Number of variables26
Number of observations3274
Missing cells324
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory666.2 KiB
Average record size in memory208.4 B

Variable types

Numeric17
Categorical9

Alerts

defaulter has constant value "0"Constant
Transaction mount is highly overall correlated with Annual Income and 5 other fieldsHigh correlation
Annual Income is highly overall correlated with Transaction mount and 5 other fieldsHigh correlation
Spending Score (1-100) is highly overall correlated with Transaction mount and 4 other fieldsHigh correlation
Atm Used PM is highly overall correlated with CustGenderHigh correlation
Credit Card PM is highly overall correlated with Saving accountsHigh correlation
Credit_Limit is highly overall correlated with Transaction mount and 5 other fieldsHigh correlation
Work_Experience is highly overall correlated with Transaction mount and 4 other fieldsHigh correlation
Graduated is highly overall correlated with Transaction mount and 4 other fieldsHigh correlation
CustGender is highly overall correlated with Atm Used PMHigh correlation
Saving accounts is highly overall correlated with Credit Card PMHigh correlation
Spending_Score is highly overall correlated with Transaction mount and 2 other fieldsHigh correlation
Work_Experience has 318 (9.7%) missing valuesMissing
Customer_ID has unique valuesUnique
Profession has 976 (29.8%) zerosZeros
state has 43 (1.3%) zerosZeros
Purpose has 916 (28.0%) zerosZeros
Atm Used PM has 138 (4.2%) zerosZeros
Credit Card PM has 662 (20.2%) zerosZeros
credit card repayment in days has 36 (1.1%) zerosZeros
Work_Experience has 58 (1.8%) zerosZeros

Reproduction

Analysis started2023-02-20 06:32:47.207709
Analysis finished2023-02-20 06:33:19.933581
Duration32.73 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

Customer_ID
Real number (ℝ)

Distinct3274
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2505.4719
Minimum0
Maximum4988
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size167.4 KiB
2023-02-20T12:03:19.992171image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile262.65
Q11275.25
median2513.5
Q33764.5
95-th percentile4744.35
Maximum4988
Range4988
Interquartile range (IQR)2489.25

Descriptive statistics

Standard deviation1439.4982
Coefficient of variation (CV)0.57454173
Kurtosis-1.2073123
Mean2505.4719
Median Absolute Deviation (MAD)1243
Skewness0.0029722227
Sum8202915
Variance2072154.9
MonotonicityStrictly increasing
2023-02-20T12:03:20.081808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
3343 1
 
< 0.1%
3332 1
 
< 0.1%
3333 1
 
< 0.1%
3334 1
 
< 0.1%
3335 1
 
< 0.1%
3337 1
 
< 0.1%
3338 1
 
< 0.1%
3339 1
 
< 0.1%
3340 1
 
< 0.1%
Other values (3264) 3264
99.7%
ValueCountFrequency (%)
0 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
6 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
ValueCountFrequency (%)
4988 1
< 0.1%
4986 1
< 0.1%
4985 1
< 0.1%
4984 1
< 0.1%
4983 1
< 0.1%
4982 1
< 0.1%
4981 1
< 0.1%
4980 1
< 0.1%
4976 1
< 0.1%
4972 1
< 0.1%

age
Real number (ℝ)

Distinct41
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.2135
Minimum20
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2023-02-20T12:03:20.167488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile27
Q133
median38
Q345
95-th percentile56
Maximum60
Range40
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.700323
Coefficient of variation (CV)0.2218706
Kurtosis-0.55449374
Mean39.2135
Median Absolute Deviation (MAD)6
Skewness0.47561981
Sum128385
Variance75.69562
MonotonicityNot monotonic
2023-02-20T12:03:20.251190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
36 188
 
5.7%
35 183
 
5.6%
33 163
 
5.0%
32 148
 
4.5%
39 144
 
4.4%
31 142
 
4.3%
34 142
 
4.3%
37 136
 
4.2%
41 125
 
3.8%
38 124
 
3.8%
Other values (31) 1779
54.3%
ValueCountFrequency (%)
20 3
 
0.1%
21 1
 
< 0.1%
22 4
 
0.1%
23 5
 
0.2%
24 30
 
0.9%
25 46
1.4%
26 54
1.6%
27 49
1.5%
28 95
2.9%
29 104
3.2%
ValueCountFrequency (%)
60 19
 
0.6%
59 32
1.0%
58 36
1.1%
57 51
1.6%
56 48
1.5%
55 40
1.2%
54 47
1.4%
53 51
1.6%
52 56
1.7%
51 56
1.7%

Graduated
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size180.2 KiB
1
1973 
0
1301 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3274
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1973
60.3%
0 1301
39.7%

Length

2023-02-20T12:03:20.334696image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-20T12:03:20.399790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1973
60.3%
0 1301
39.7%

Most occurring characters

ValueCountFrequency (%)
1 1973
60.3%
0 1301
39.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3274
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1973
60.3%
0 1301
39.7%

Most occurring scripts

ValueCountFrequency (%)
Common 3274
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1973
60.3%
0 1301
39.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1973
60.3%
0 1301
39.7%

marital
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size180.2 KiB
2
2128 
0
727 
1
419 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3274
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 2128
65.0%
0 727
 
22.2%
1 419
 
12.8%

Length

2023-02-20T12:03:20.458468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-20T12:03:20.524164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2 2128
65.0%
0 727
 
22.2%
1 419
 
12.8%

Most occurring characters

ValueCountFrequency (%)
2 2128
65.0%
0 727
 
22.2%
1 419
 
12.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3274
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2128
65.0%
0 727
 
22.2%
1 419
 
12.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3274
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 2128
65.0%
0 727
 
22.2%
1 419
 
12.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2128
65.0%
0 727
 
22.2%
1 419
 
12.8%

Profession
Real number (ℝ)

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8958461
Minimum0
Maximum9
Zeros976
Zeros (%)29.8%
Negative0
Negative (%)0.0%
Memory size167.4 KiB
2023-02-20T12:03:20.582136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q35
95-th percentile7
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.5915256
Coefficient of variation (CV)0.89491137
Kurtosis-0.94109111
Mean2.8958461
Median Absolute Deviation (MAD)2
Skewness0.44079977
Sum9481
Variance6.7160047
MonotonicityNot monotonic
2023-02-20T12:03:20.643790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 976
29.8%
5 551
16.8%
3 412
12.6%
1 294
 
9.0%
2 292
 
8.9%
7 251
 
7.7%
4 249
 
7.6%
8 115
 
3.5%
6 89
 
2.7%
9 45
 
1.4%
ValueCountFrequency (%)
0 976
29.8%
1 294
 
9.0%
2 292
 
8.9%
3 412
12.6%
4 249
 
7.6%
5 551
16.8%
6 89
 
2.7%
7 251
 
7.7%
8 115
 
3.5%
9 45
 
1.4%
ValueCountFrequency (%)
9 45
 
1.4%
8 115
 
3.5%
7 251
 
7.7%
6 89
 
2.7%
5 551
16.8%
4 249
 
7.6%
3 412
12.6%
2 292
 
8.9%
1 294
 
9.0%
0 976
29.8%

defaulter
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size180.2 KiB
0
3274 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3274
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3274
100.0%

Length

2023-02-20T12:03:20.717410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-20T12:03:20.795491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0 3274
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3274
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3274
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3274
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3274
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3274
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3274
100.0%

loan
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size180.2 KiB
0
2790 
2
484 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3274
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row2
5th row0

Common Values

ValueCountFrequency (%)
0 2790
85.2%
2 484
 
14.8%

Length

2023-02-20T12:03:20.909933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-20T12:03:21.007020image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0 2790
85.2%
2 484
 
14.8%

Most occurring characters

ValueCountFrequency (%)
0 2790
85.2%
2 484
 
14.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3274
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2790
85.2%
2 484
 
14.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3274
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2790
85.2%
2 484
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2790
85.2%
2 484
 
14.8%

CustGender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size180.2 KiB
1
2392 
0
882 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3274
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 2392
73.1%
0 882
 
26.9%

Length

2023-02-20T12:03:21.079623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-20T12:03:21.142380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1 2392
73.1%
0 882
 
26.9%

Most occurring characters

ValueCountFrequency (%)
1 2392
73.1%
0 882
 
26.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3274
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2392
73.1%
0 882
 
26.9%

Most occurring scripts

ValueCountFrequency (%)
Common 3274
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2392
73.1%
0 882
 
26.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2392
73.1%
0 882
 
26.9%

state
Real number (ℝ)

Distinct54
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.288943
Minimum0
Maximum53
Zeros43
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size167.4 KiB
2023-02-20T12:03:21.206466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q19
median23
Q338
95-th percentile50
Maximum53
Range53
Interquartile range (IQR)29

Descriptive statistics

Standard deviation16.036571
Coefficient of variation (CV)0.6602416
Kurtosis-1.2992498
Mean24.288943
Median Absolute Deviation (MAD)14
Skewness0.16182112
Sum79522
Variance257.1716
MonotonicityNot monotonic
2023-02-20T12:03:21.290143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 381
 
11.6%
50 190
 
5.8%
9 184
 
5.6%
33 168
 
5.1%
10 140
 
4.3%
5 120
 
3.7%
45 105
 
3.2%
22 101
 
3.1%
14 94
 
2.9%
31 92
 
2.8%
Other values (44) 1699
51.9%
ValueCountFrequency (%)
0 43
 
1.3%
1 8
 
0.2%
2 65
 
2.0%
3 41
 
1.3%
4 381
11.6%
5 120
 
3.7%
6 22
 
0.7%
7 9
 
0.3%
8 7
 
0.2%
9 184
5.6%
ValueCountFrequency (%)
53 10
 
0.3%
52 66
 
2.0%
51 3
 
0.1%
50 190
5.8%
49 68
 
2.1%
48 2
 
0.1%
47 21
 
0.6%
46 48
 
1.5%
45 105
3.2%
44 59
 
1.8%

Transaction mount
Real number (ℝ)

Distinct101
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.314294
Minimum30
Maximum130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2023-02-20T12:03:21.381910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile36
Q154.25
median72
Q386
95-th percentile109
Maximum130
Range100
Interquartile range (IQR)31.75

Descriptive statistics

Standard deviation21.712757
Coefficient of variation (CV)0.3044657
Kurtosis-0.51533447
Mean71.314294
Median Absolute Deviation (MAD)16
Skewness0.20325683
Sum233483
Variance471.44381
MonotonicityNot monotonic
2023-02-20T12:03:21.486919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78 87
 
2.7%
70 80
 
2.4%
80 77
 
2.4%
72 74
 
2.3%
74 69
 
2.1%
56 69
 
2.1%
52 67
 
2.0%
50 66
 
2.0%
60 65
 
2.0%
77 64
 
2.0%
Other values (91) 2556
78.1%
ValueCountFrequency (%)
30 20
0.6%
31 27
0.8%
32 26
0.8%
33 24
0.7%
34 30
0.9%
35 23
0.7%
36 28
0.9%
37 22
0.7%
38 24
0.7%
39 37
1.1%
ValueCountFrequency (%)
130 4
0.1%
129 5
0.2%
128 5
0.2%
127 4
0.1%
126 5
0.2%
125 5
0.2%
124 2
 
0.1%
123 3
0.1%
122 2
 
0.1%
121 5
0.2%

CustAccountBalance
Real number (ℝ)

Distinct3067
Distinct (%)93.8%
Missing6
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean112559.64
Minimum0
Maximum14131195
Zeros8
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2023-02-20T12:03:21.572248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile142.8425
Q15650.7475
median18653.125
Q365487.02
95-th percentile440463.42
Maximum14131195
Range14131195
Interquartile range (IQR)59836.272

Descriptive statistics

Standard deviation462677.7
Coefficient of variation (CV)4.1105117
Kurtosis370.15843
Mean112559.64
Median Absolute Deviation (MAD)16682.64
Skewness15.733335
Sum3.6784489 × 108
Variance2.1407066 × 1011
MonotonicityNot monotonic
2023-02-20T12:03:21.655024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
0.2%
27021.05 7
 
0.2%
324551.5 4
 
0.1%
4573.28 4
 
0.1%
96276.9 4
 
0.1%
4102.18 3
 
0.1%
7371.9 3
 
0.1%
11614.1 3
 
0.1%
36537.44 3
 
0.1%
179848.2 3
 
0.1%
Other values (3057) 3226
98.5%
(Missing) 6
 
0.2%
ValueCountFrequency (%)
0 8
0.2%
0.02 1
 
< 0.1%
0.25 1
 
< 0.1%
0.61 1
 
< 0.1%
0.65 1
 
< 0.1%
0.7 1
 
< 0.1%
0.78 1
 
< 0.1%
1 1
 
< 0.1%
1.46 1
 
< 0.1%
1.47 1
 
< 0.1%
ValueCountFrequency (%)
14131195.02 1
< 0.1%
10576939.93 1
< 0.1%
6556608.08 1
< 0.1%
6153347.84 1
< 0.1%
4682213.6 1
< 0.1%
4027066.25 1
< 0.1%
3659161.33 1
< 0.1%
3656641.02 1
< 0.1%
3654866.88 1
< 0.1%
3482113.5 1
< 0.1%

Annual Income
Real number (ℝ)

Distinct101
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.519853
Minimum50
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2023-02-20T12:03:21.743749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile59
Q176
median95
Q3108
95-th percentile129
Maximum150
Range100
Interquartile range (IQR)32

Descriptive statistics

Standard deviation21.31871
Coefficient of variation (CV)0.22795919
Kurtosis-0.58432069
Mean93.519853
Median Absolute Deviation (MAD)15
Skewness0.067058345
Sum306184
Variance454.48738
MonotonicityNot monotonic
2023-02-20T12:03:21.829189image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 82
 
2.5%
100 80
 
2.4%
105 72
 
2.2%
110 72
 
2.2%
109 70
 
2.1%
90 68
 
2.1%
108 67
 
2.0%
92 65
 
2.0%
91 63
 
1.9%
103 62
 
1.9%
Other values (91) 2573
78.6%
ValueCountFrequency (%)
50 17
0.5%
51 19
0.6%
52 14
0.4%
53 19
0.6%
54 21
0.6%
55 17
0.5%
56 20
0.6%
57 14
0.4%
58 13
0.4%
59 17
0.5%
ValueCountFrequency (%)
150 4
0.1%
149 2
 
0.1%
148 3
0.1%
147 5
0.2%
146 7
0.2%
145 4
0.1%
144 3
0.1%
143 2
 
0.1%
142 5
0.2%
141 3
0.1%

Spending Score (1-100)
Real number (ℝ)

Distinct101
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.480443
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2023-02-20T12:03:21.912699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16.65
Q144
median61
Q372
95-th percentile86
Maximum100
Range99
Interquartile range (IQR)28

Descriptive statistics

Standard deviation21.217916
Coefficient of variation (CV)0.37566837
Kurtosis-0.29047479
Mean56.480443
Median Absolute Deviation (MAD)13
Skewness-0.613238
Sum184916.97
Variance450.19997
MonotonicityNot monotonic
2023-02-20T12:03:21.999862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 96
 
2.9%
65 93
 
2.8%
50 88
 
2.7%
68 82
 
2.5%
61 82
 
2.5%
69 80
 
2.4%
66 79
 
2.4%
62 77
 
2.4%
60 75
 
2.3%
67 73
 
2.2%
Other values (91) 2449
74.8%
ValueCountFrequency (%)
1 13
0.4%
2 11
0.3%
3 10
0.3%
4 17
0.5%
5 7
0.2%
6 11
0.3%
7 5
 
0.2%
8 7
0.2%
9 15
0.5%
10 7
0.2%
ValueCountFrequency (%)
100 5
 
0.2%
99 3
 
0.1%
98 2
 
0.1%
97 6
 
0.2%
96 15
0.5%
95 3
 
0.1%
94 5
 
0.2%
93 5
 
0.2%
92 8
0.2%
91 4
 
0.1%

Housing
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size180.2 KiB
1
2373 
2
493 
0
408 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3274
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 2373
72.5%
2 493
 
15.1%
0 408
 
12.5%

Length

2023-02-20T12:03:22.077523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-20T12:03:22.142773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1 2373
72.5%
2 493
 
15.1%
0 408
 
12.5%

Most occurring characters

ValueCountFrequency (%)
1 2373
72.5%
2 493
 
15.1%
0 408
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3274
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2373
72.5%
2 493
 
15.1%
0 408
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Common 3274
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2373
72.5%
2 493
 
15.1%
0 408
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2373
72.5%
2 493
 
15.1%
0 408
 
12.5%

Saving accounts
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size180.2 KiB
0
1978 
4
601 
1
339 
2
198 
3
 
158

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3274
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row0
3rd row0
4th row0
5th row2

Common Values

ValueCountFrequency (%)
0 1978
60.4%
4 601
 
18.4%
1 339
 
10.4%
2 198
 
6.0%
3 158
 
4.8%

Length

2023-02-20T12:03:22.203775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-20T12:03:22.272279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1978
60.4%
4 601
 
18.4%
1 339
 
10.4%
2 198
 
6.0%
3 158
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 1978
60.4%
4 601
 
18.4%
1 339
 
10.4%
2 198
 
6.0%
3 158
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3274
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1978
60.4%
4 601
 
18.4%
1 339
 
10.4%
2 198
 
6.0%
3 158
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3274
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1978
60.4%
4 601
 
18.4%
1 339
 
10.4%
2 198
 
6.0%
3 158
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1978
60.4%
4 601
 
18.4%
1 339
 
10.4%
2 198
 
6.0%
3 158
 
4.8%

Checking account
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size180.2 KiB
3
1254 
1
912 
0
890 
2
218 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3274
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row3
3rd row0
4th row0
5th row3

Common Values

ValueCountFrequency (%)
3 1254
38.3%
1 912
27.9%
0 890
27.2%
2 218
 
6.7%

Length

2023-02-20T12:03:22.338037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-20T12:03:22.404117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
3 1254
38.3%
1 912
27.9%
0 890
27.2%
2 218
 
6.7%

Most occurring characters

ValueCountFrequency (%)
3 1254
38.3%
1 912
27.9%
0 890
27.2%
2 218
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3274
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1254
38.3%
1 912
27.9%
0 890
27.2%
2 218
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Common 3274
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1254
38.3%
1 912
27.9%
0 890
27.2%
2 218
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1254
38.3%
1 912
27.9%
0 890
27.2%
2 218
 
6.7%

Credit amount
Real number (ℝ)

Distinct2888
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7931.0761
Minimum250
Maximum18488
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2023-02-20T12:03:22.475793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile899.65
Q12910
median7057
Q312599.75
95-th percentile17316.7
Maximum18488
Range18238
Interquartile range (IQR)9689.75

Descriptive statistics

Standard deviation5466.5478
Coefficient of variation (CV)0.68925676
Kurtosis-1.183389
Mean7931.0761
Median Absolute Deviation (MAD)4648
Skewness0.34376593
Sum25966343
Variance29883145
MonotonicityNot monotonic
2023-02-20T12:03:22.559558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2384 4
 
0.1%
10073 4
 
0.1%
1262 4
 
0.1%
15576 4
 
0.1%
6527 4
 
0.1%
5360 4
 
0.1%
1158 4
 
0.1%
17098 3
 
0.1%
3414 3
 
0.1%
3154 3
 
0.1%
Other values (2878) 3237
98.9%
ValueCountFrequency (%)
250 1
< 0.1%
259 1
< 0.1%
270 1
< 0.1%
275 1
< 0.1%
276 1
< 0.1%
290 1
< 0.1%
318 1
< 0.1%
329 1
< 0.1%
336 1
< 0.1%
338 2
0.1%
ValueCountFrequency (%)
18488 1
< 0.1%
18486 1
< 0.1%
18470 1
< 0.1%
18467 1
< 0.1%
18464 1
< 0.1%
18457 1
< 0.1%
18456 1
< 0.1%
18454 1
< 0.1%
18446 1
< 0.1%
18444 1
< 0.1%

Duration
Real number (ℝ)

Distinct60
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.578803
Minimum2
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2023-02-20T12:03:22.646316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q112
median24
Q339
95-th percentile55
Maximum72
Range70
Interquartile range (IQR)27

Descriptive statistics

Standard deviation15.837841
Coefficient of variation (CV)0.59588241
Kurtosis-0.95226799
Mean26.578803
Median Absolute Deviation (MAD)12
Skewness0.42238229
Sum87019
Variance250.8372
MonotonicityNot monotonic
2023-02-20T12:03:22.730071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 259
 
7.9%
24 243
 
7.4%
18 188
 
5.7%
6 127
 
3.9%
15 123
 
3.8%
36 122
 
3.7%
48 100
 
3.1%
30 87
 
2.7%
9 75
 
2.3%
10 73
 
2.2%
Other values (50) 1877
57.3%
ValueCountFrequency (%)
2 44
 
1.3%
3 45
 
1.4%
4 38
 
1.2%
5 31
 
0.9%
6 127
3.9%
7 42
 
1.3%
8 52
1.6%
9 75
2.3%
10 73
2.2%
11 38
 
1.2%
ValueCountFrequency (%)
72 1
 
< 0.1%
60 20
0.6%
59 30
0.9%
58 34
1.0%
57 35
1.1%
56 30
0.9%
55 37
1.1%
54 33
1.0%
53 36
1.1%
52 36
1.1%

Purpose
Real number (ℝ)

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9526573
Minimum0
Maximum7
Zeros916
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size167.4 KiB
2023-02-20T12:03:22.800149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile5
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7068778
Coefficient of variation (CV)0.87413073
Kurtosis-0.15378723
Mean1.9526573
Median Absolute Deviation (MAD)1
Skewness0.61966766
Sum6393
Variance2.9134317
MonotonicityNot monotonic
2023-02-20T12:03:22.854734image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 1089
33.3%
0 916
28.0%
1 602
18.4%
2 309
 
9.4%
5 199
 
6.1%
6 82
 
2.5%
4 40
 
1.2%
7 37
 
1.1%
ValueCountFrequency (%)
0 916
28.0%
1 602
18.4%
2 309
 
9.4%
3 1089
33.3%
4 40
 
1.2%
5 199
 
6.1%
6 82
 
2.5%
7 37
 
1.1%
ValueCountFrequency (%)
7 37
 
1.1%
6 82
 
2.5%
5 199
 
6.1%
4 40
 
1.2%
3 1089
33.3%
2 309
 
9.4%
1 602
18.4%
0 916
28.0%

Atm Used PM
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1533293
Minimum0
Maximum10
Zeros138
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2023-02-20T12:03:22.919569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile7
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0504654
Coefficient of variation (CV)0.493692
Kurtosis0.089730921
Mean4.1533293
Median Absolute Deviation (MAD)1
Skewness0.32292647
Sum13598
Variance4.2044084
MonotonicityNot monotonic
2023-02-20T12:03:22.983495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
4 697
21.3%
3 669
20.4%
5 452
13.8%
2 390
11.9%
6 343
10.5%
7 338
10.3%
0 138
 
4.2%
1 111
 
3.4%
10 50
 
1.5%
8 46
 
1.4%
ValueCountFrequency (%)
0 138
 
4.2%
1 111
 
3.4%
2 390
11.9%
3 669
20.4%
4 697
21.3%
5 452
13.8%
6 343
10.5%
7 338
10.3%
8 46
 
1.4%
9 40
 
1.2%
ValueCountFrequency (%)
10 50
 
1.5%
9 40
 
1.2%
8 46
 
1.4%
7 338
10.3%
6 343
10.5%
5 452
13.8%
4 697
21.3%
3 669
20.4%
2 390
11.9%
1 111
 
3.4%

Credit Card PM
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.446854
Minimum0
Maximum13
Zeros662
Zeros (%)20.2%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2023-02-20T12:03:23.045008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile7
Maximum13
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.3576654
Coefficient of variation (CV)0.96354969
Kurtosis2.4069794
Mean2.446854
Median Absolute Deviation (MAD)1
Skewness1.4793453
Sum8011
Variance5.5585862
MonotonicityNot monotonic
2023-02-20T12:03:23.111014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 688
21.0%
2 673
20.6%
0 662
20.2%
1 650
19.9%
7 141
 
4.3%
6 118
 
3.6%
5 106
 
3.2%
4 85
 
2.6%
8 77
 
2.4%
10 27
 
0.8%
Other values (4) 47
 
1.4%
ValueCountFrequency (%)
0 662
20.2%
1 650
19.9%
2 673
20.6%
3 688
21.0%
4 85
 
2.6%
5 106
 
3.2%
6 118
 
3.6%
7 141
 
4.3%
8 77
 
2.4%
9 24
 
0.7%
ValueCountFrequency (%)
13 13
 
0.4%
12 5
 
0.2%
11 5
 
0.2%
10 27
 
0.8%
9 24
 
0.7%
8 77
2.4%
7 141
4.3%
6 118
3.6%
5 106
3.2%
4 85
2.6%
Distinct101
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.81796
Minimum0
Maximum100
Zeros36
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2023-02-20T12:03:23.187522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q124
median50
Q375
95-th percentile96
Maximum100
Range100
Interquartile range (IQR)51

Descriptive statistics

Standard deviation29.417533
Coefficient of variation (CV)0.59050055
Kurtosis-1.2093528
Mean49.81796
Median Absolute Deviation (MAD)25
Skewness0.0042568014
Sum163104
Variance865.39123
MonotonicityNot monotonic
2023-02-20T12:03:23.270035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 47
 
1.4%
6 47
 
1.4%
64 46
 
1.4%
34 44
 
1.3%
53 43
 
1.3%
59 42
 
1.3%
70 42
 
1.3%
16 41
 
1.3%
4 40
 
1.2%
94 40
 
1.2%
Other values (91) 2842
86.8%
ValueCountFrequency (%)
0 36
1.1%
1 25
0.8%
2 34
1.0%
3 38
1.2%
4 40
1.2%
5 39
1.2%
6 47
1.4%
7 32
1.0%
8 39
1.2%
9 23
0.7%
ValueCountFrequency (%)
100 35
1.1%
99 31
0.9%
98 38
1.2%
97 39
1.2%
96 28
0.9%
95 34
1.0%
94 40
1.2%
93 34
1.0%
92 33
1.0%
91 27
0.8%

Credit_Limit
Real number (ℝ)

Distinct3032
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14830.944
Minimum1504
Maximum34968
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2023-02-20T12:03:23.352886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1504
5-th percentile2272.55
Q15455.75
median16018
Q322390.5
95-th percentile28307.05
Maximum34968
Range33464
Interquartile range (IQR)16934.75

Descriptive statistics

Standard deviation8862.7856
Coefficient of variation (CV)0.59758741
Kurtosis-1.2143734
Mean14830.944
Median Absolute Deviation (MAD)7458
Skewness0.019764679
Sum48556512
Variance78548969
MonotonicityNot monotonic
2023-02-20T12:03:23.434550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22484 4
 
0.1%
22183 3
 
0.1%
24248 3
 
0.1%
22848 3
 
0.1%
21023 3
 
0.1%
2278 3
 
0.1%
2237 3
 
0.1%
21487 3
 
0.1%
20996 3
 
0.1%
4995 3
 
0.1%
Other values (3022) 3243
99.1%
ValueCountFrequency (%)
1504 1
< 0.1%
1505 1
< 0.1%
1515 2
0.1%
1516 1
< 0.1%
1517 1
< 0.1%
1518 1
< 0.1%
1523 1
< 0.1%
1524 1
< 0.1%
1538 1
< 0.1%
1542 1
< 0.1%
ValueCountFrequency (%)
34968 1
< 0.1%
34887 1
< 0.1%
34834 1
< 0.1%
34767 1
< 0.1%
34766 1
< 0.1%
34732 1
< 0.1%
34717 1
< 0.1%
34484 1
< 0.1%
34443 1
< 0.1%
34434 1
< 0.1%

Work_Experience
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct15
Distinct (%)0.5%
Missing318
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean9.6224628
Minimum0
Maximum14
Zeros58
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2023-02-20T12:03:23.507552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median10
Q312
95-th percentile14
Maximum14
Range14
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.5800098
Coefficient of variation (CV)0.37204714
Kurtosis0.18927686
Mean9.6224628
Median Absolute Deviation (MAD)2
Skewness-0.94111214
Sum28444
Variance12.81647
MonotonicityNot monotonic
2023-02-20T12:03:23.567056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
10 531
16.2%
11 385
11.8%
14 353
10.8%
12 329
10.0%
13 327
10.0%
9 168
 
5.1%
6 164
 
5.0%
8 161
 
4.9%
7 128
 
3.9%
3 86
 
2.6%
Other values (5) 324
9.9%
(Missing) 318
9.7%
ValueCountFrequency (%)
0 58
 
1.8%
1 65
 
2.0%
2 60
 
1.8%
3 86
2.6%
4 70
2.1%
5 71
2.2%
6 164
5.0%
7 128
3.9%
8 161
4.9%
9 168
5.1%
ValueCountFrequency (%)
14 353
10.8%
13 327
10.0%
12 329
10.0%
11 385
11.8%
10 531
16.2%
9 168
 
5.1%
8 161
 
4.9%
7 128
 
3.9%
6 164
 
5.0%
5 71
 
2.2%

Spending_Score
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size180.2 KiB
2
1998 
0
805 
1
471 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3274
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 1998
61.0%
0 805
24.6%
1 471
 
14.4%

Length

2023-02-20T12:03:23.633563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-20T12:03:23.698562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2 1998
61.0%
0 805
24.6%
1 471
 
14.4%

Most occurring characters

ValueCountFrequency (%)
2 1998
61.0%
0 805
24.6%
1 471
 
14.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3274
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1998
61.0%
0 805
24.6%
1 471
 
14.4%

Most occurring scripts

ValueCountFrequency (%)
Common 3274
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1998
61.0%
0 805
24.6%
1 471
 
14.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1998
61.0%
0 805
24.6%
1 471
 
14.4%

Family_Size
Real number (ℝ)

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2962737
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2023-02-20T12:03:23.752068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.582888
Coefficient of variation (CV)0.48020528
Kurtosis-1.0209996
Mean3.2962737
Median Absolute Deviation (MAD)1
Skewness0.32961284
Sum10792
Variance2.5055345
MonotonicityNot monotonic
2023-02-20T12:03:23.808070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 811
24.8%
3 778
23.8%
6 440
13.4%
5 422
12.9%
4 421
12.9%
1 402
12.3%
ValueCountFrequency (%)
1 402
12.3%
2 811
24.8%
3 778
23.8%
4 421
12.9%
5 422
12.9%
6 440
13.4%
ValueCountFrequency (%)
6 440
13.4%
5 422
12.9%
4 421
12.9%
3 778
23.8%
2 811
24.8%
1 402
12.3%

Interactions

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2023-02-20T12:02:55.835246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:57.213532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:58.530456image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:00.566107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:01.846188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:03.355388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:06.342607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:08.591143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:10.065102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:11.501907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:12.911182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:14.872726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:16.654073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:18.514988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:49.549860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:52.169922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:54.045641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:55.913024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:57.287606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:58.611962image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:00.639936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:01.924702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:03.541420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:06.516324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:08.670646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:10.144869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:11.581411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:12.988180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:15.021915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:16.732195image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:18.590058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:49.722039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:52.324972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:54.123387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:55.988026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:57.361206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:58.694470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:00.715625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:02.002213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:03.732574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:06.682976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:08.753178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:10.226373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:11.656916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:13.067689image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:15.186540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:17.206013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:18.662724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:49.898047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:52.447510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:54.198090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:56.061530image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:57.433960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:59.487552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:00.786622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:02.076212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:03.908216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:06.843024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:08.830687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:10.328917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:11.730422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:13.163296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:15.338735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:17.302536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:18.738230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:50.094247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:52.527260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:54.279104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:56.136036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:57.509551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:59.571063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:00.862285image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:02.151956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:04.088828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:06.929527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:08.909194image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:10.403423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:11.803927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:13.240982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:15.489431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:17.389035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:18.810735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:50.247379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:52.609990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:54.354769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:56.210873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:57.581467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:59.651582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:00.937058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:02.234651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:04.247002image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:07.009034image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:08.994204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:10.475423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:11.878927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:13.316486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:15.621595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:17.469542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:18.897738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:50.398422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:52.692806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:54.509936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:56.310897image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:57.657970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:59.740086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:01.012804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:02.350176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:04.405991image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:07.130708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:09.075708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:10.551929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:11.958026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:13.524040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:15.743056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:17.561055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:18.976247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:50.598815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:52.790323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:54.672990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:56.473158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:57.736476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:02:59.822608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:01.089966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:02.455682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:04.588594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:07.296309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:09.157212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:10.626597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:12.039540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:13.656586image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:15.845882image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-20T12:03:17.650571image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2023-02-20T12:03:23.890574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Customer_IDageProfessionstateTransaction mountCustAccountBalanceAnnual IncomeSpending Score (1-100)Credit amountDurationPurposeAtm Used PMCredit Card PMcredit card repayment in daysCredit_LimitWork_ExperienceFamily_SizeGraduatedmaritalloanCustGenderHousingSaving accountsChecking accountSpending_Score
Customer_ID1.000-0.103-0.0190.0900.0400.0160.0420.0280.3680.2100.008-0.017-0.0290.0130.0300.043-0.0170.0000.0380.0000.0640.0000.0000.0000.000
age-0.1031.0000.003-0.034-0.032-0.034-0.037-0.030-0.077-0.0410.0130.065-0.0150.006-0.049-0.0270.1110.0000.2700.0500.0000.0000.0150.0000.010
Profession-0.0190.0031.000-0.017-0.188-0.007-0.185-0.215-0.008-0.015-0.024-0.0180.029-0.014-0.167-0.215-0.0120.3830.0000.0000.0000.0250.0000.0000.440
state0.090-0.034-0.0171.000-0.033-0.018-0.028-0.0250.066-0.000-0.007-0.0300.032-0.017-0.017-0.014-0.0090.0000.0290.0440.0000.0000.0000.0000.020
Transaction mount0.040-0.032-0.188-0.0331.000-0.0010.7930.7920.0450.018-0.020-0.0260.052-0.0170.7000.626-0.1540.6120.2750.0000.0000.0220.0270.0000.616
CustAccountBalance0.016-0.034-0.007-0.018-0.0011.0000.0320.012-0.0100.0310.001-0.056-0.027-0.0190.0220.025-0.0090.0000.0000.0430.0120.0000.0210.0040.000
Annual Income0.042-0.037-0.185-0.0280.7930.0321.0000.7640.0290.029-0.013-0.0270.036-0.0250.9010.763-0.1210.6170.2190.0000.0000.0130.0000.0000.617
Spending Score (1-100)0.028-0.030-0.215-0.0250.7920.0120.7641.0000.0250.032-0.034-0.0070.031-0.0090.6710.624-0.0920.8240.3870.0190.0000.0000.0120.0070.479
Credit amount0.368-0.077-0.0080.0660.045-0.0100.0290.0251.0000.1700.062-0.0050.004-0.0080.0310.026-0.0190.0100.0240.0000.0230.0300.0080.0220.012
Duration0.210-0.041-0.015-0.0000.0180.0310.0290.0320.1701.0000.025-0.004-0.007-0.0090.0230.045-0.0180.0320.0000.0000.0240.0300.0420.0000.021
Purpose0.0080.013-0.024-0.007-0.0200.001-0.013-0.0340.0620.0251.000-0.0190.012-0.009-0.005-0.0010.0210.0000.0000.0000.0070.0230.0800.1140.021
Atm Used PM-0.0170.065-0.018-0.030-0.026-0.056-0.027-0.007-0.005-0.004-0.0191.0000.198-0.002-0.028-0.0070.1960.0300.2770.0000.5410.0000.2500.0580.096
Credit Card PM-0.029-0.0150.0290.0320.052-0.0270.0360.0310.004-0.0070.0120.1981.000-0.0000.0330.027-0.0260.0000.1190.0000.1020.0340.5380.0950.074
credit card repayment in days0.0130.006-0.014-0.017-0.017-0.019-0.025-0.009-0.008-0.009-0.009-0.002-0.0001.000-0.030-0.023-0.0150.0440.0140.0370.0000.0240.0260.0390.018
Credit_Limit0.030-0.049-0.167-0.0170.7000.0220.9010.6710.0310.023-0.005-0.0280.033-0.0301.0000.663-0.1110.5370.1440.0000.0000.0000.0180.0000.529
Work_Experience0.043-0.027-0.215-0.0140.6260.0250.7630.6240.0260.045-0.001-0.0070.027-0.0230.6631.000-0.0810.6000.1090.0040.0000.0000.0000.0190.365
Family_Size-0.0170.111-0.012-0.009-0.154-0.009-0.121-0.092-0.019-0.0180.0210.196-0.026-0.015-0.111-0.0811.0000.0260.4830.0150.0000.0000.0150.0240.000
Graduated0.0000.0000.3830.0000.6120.0000.6170.8240.0100.0320.0000.0300.0000.0440.5370.6000.0261.0000.0000.0000.0000.0000.0010.0000.150
marital0.0380.2700.0000.0290.2750.0000.2190.3870.0240.0000.0000.2770.1190.0140.1440.1090.4830.0001.0000.0400.0000.0000.0190.0170.000
loan0.0000.0500.0000.0440.0000.0430.0000.0190.0000.0000.0000.0000.0000.0370.0000.0040.0150.0000.0401.0000.0000.0290.0000.0000.000
CustGender0.0640.0000.0000.0000.0000.0120.0000.0000.0230.0240.0070.5410.1020.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
Housing0.0000.0000.0250.0000.0220.0000.0130.0000.0300.0300.0230.0000.0340.0240.0000.0000.0000.0000.0000.0290.0001.0000.0180.0000.009
Saving accounts0.0000.0150.0000.0000.0270.0210.0000.0120.0080.0420.0800.2500.5380.0260.0180.0000.0150.0010.0190.0000.0000.0181.0000.1680.000
Checking account0.0000.0000.0000.0000.0000.0040.0000.0070.0220.0000.1140.0580.0950.0390.0000.0190.0240.0000.0170.0000.0000.0000.1681.0000.034
Spending_Score0.0000.0100.4400.0200.6160.0000.6170.4790.0120.0210.0210.0960.0740.0180.5290.3650.0000.1500.0000.0000.0000.0090.0000.0341.000

Missing values

2023-02-20T12:03:19.147282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-20T12:03:19.746305image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-02-20T12:03:19.881071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Customer_IDageGraduatedmaritalProfessiondefaulterloanCustGenderstateTransaction mountCustAccountBalanceAnnual IncomeSpending Score (1-100)HousingSaving accountsChecking accountCredit amountDurationPurposeAtm Used PMCredit Card PMcredit card repayment in daysCredit_LimitWork_ExperienceSpending_ScoreFamily_Size
0056025000504417819.055131.0140116960021437602.025
2237122000507417874.448470.010320961250316194576.024
334012700014106866503.2112577.0000788242133762400311.012
4456123020501176714.4313180.00004870243335630667NaN12
66591250001452973.4610664.0123283524150452033710.025
882410200027414906.9610580.0133305912067912437813.021
992510000141084279.2212487.0101523430340372274312.001
111125005001144714613.466222.02004308482224823032.021
121229004021503132274.787628.0101156712022511708210.022
13135701700023259950.44709.0100119924350294284NaN23
Customer_IDageGraduatedmaritalProfessiondefaulterloanCustGenderstateTransaction mountCustAccountBalanceAnnual IncomeSpending Score (1-100)HousingSaving accountsChecking accountCredit amountDurationPurposeAtm Used PMCredit Card PMcredit card repayment in daysCredit_LimitWork_ExperienceSpending_ScoreFamily_Size
49824972441100213910416014.3312077.0100411024132942326914.003
498649763712700139634893.268557.010041692413189161476.022
4990498032100001398213700.6110077.0121321318048751395910.022
499149813500800139488961.869027.0101443918220941674812.023
4992498250125001398064313.918758.0102394910332541777610.024
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